Abstract
BackgroundReed has high lignin content, wide distribution and low cost. It is an ideal raw material for replacing wood in the paper industry. Reeds are rich in resources, but the density of reeds is low, leading to high transportation and storage costs. This paper aims to study the compression process of reeds and the creep behaviour of compressed reeds, and provide theoretical guidance for the reed compressor management, bundling equipment and the stability of compressed reed bales.ResultsWe have established a multi-layer perceptron network prediction model for the creep characteristics of reeds, and the prediction rate R2 of this model is greater than 0.997. The constitutive equation, constitutive coefficient and creep quaternary model of the reed creep process were established by using the prediction model. The creep behaviour of the reed bale is positively correlated with the initial maximum compressive stress (σ0). During the creep of the reed, the elastic power and the viscous resistance restrict each other. The results show that the proportion of elastic strain in the initial stage is the largest, and gradually decreases to 99.19% over time. The viscoelastic strain increases rapidly with time, then slowly increases, and finally stabilizes to 0.69%, while the plastic strain accounts for the proportion of the total strain. The specific gravity of the reed increases linearly with the increase of creep time, and finally accounts for 0.39%, indicating that as time increases, the damage of the reed's own structure gradually increases.ConclusionsWe studied the relationship between the strain and time of the reed and the strain and creep behaviour of the reed bag under different holding forces under constant force. It is proved that the multi-layer perceptron network is better than the support vector machine regression in predicting the characteristics of reed materials. The three stages of elasticity, viscoelasticity and plasticity in the process of reed creep are analysed in detail. This article opens up a new way for using machine learning methods to predict the mechanical properties of materials. The proposed prediction model provides new ideas for the characterization of material characteristics.
Highlights
Reed has high lignin content, wide distribution and low cost
Optimization results of hyper‐parameters The cross-validation method and genetic algorithm mentioned in Sect. 2.3.4 were used to optimize the hyperparameters of the multi-layer perceptron (MLP) and support vector machine regression (SVR) machine learning prediction models
The blue and red lines in the Fig. are the creep curves predicted by MLP and SVR, respectively, and the shaded area refers to the predicted envelope range after 10 training sets
Summary
Reed has high lignin content, wide distribution and low cost. It is an ideal raw material for replacing wood in the paper industry. Over-cultivation of forests and pastures has led to enormous environmental problems [1, 2] Due to their high lignin content, wide distribution, and low cost, reeds are an excellent candidate for a raw. Krstic et al [10] investigated the compression properties of treated corn stover, grassland rush and switchgrass These researchers found that when the moisture content is 16–20%, the yield strength of these materials is relatively low, but the particles of these materials have a higher compaction density. Through rheological tests on rice seedling stems, Scharenbroch et al [16] concluded that the occurrence of a creep process and the plastic strain were positively correlated with the creep time and initial stress affecting the rice seedling stems These studies demonstrate that the compression of straw bales is time-dependent [17, 18]. Analytical studies on the post-compression creep properties of tall, thick and hard stalk crops (e.g., reeds) have yet to be reported
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